When recognizing an object based on an image, normally, there is a tendency that the recognition rate is largely deteriorated when there is partial concealment generated in the object due to obstacles and noises. Non-Patent Document 1 and Non-Patent Document 2 depict examples of the object recognition system that is not susceptible to partial concealment.
Non-Patent Document 1 is an example in which a face is recognized based on an image. Operations of the object recognition system depicted in Non-Patent Document 1 are roughly as follows. First, an inputted face image is divided into partial regions, and feature extracting processing and recognition processing are conducted for each region. Then, only a certain number of regions exhibiting high similarity among recognition results of the individual partial regions are used to eliminate the region with concealment so as to calculate the final similarity. When this technique is used, it is almost possible to perform recognition even if there is partial concealment. However, it is not possible with this technique to simultaneously deal with both cases where the concealed region is wide and where the concealed region is narrow. Further, information of the regions with low similarity is not reflected upon the final result at all, so that there is a possibility of loosing useful information for recognition.
Non-Patent Document 2 uses a method which recollects a lost part of data by using a self-associative memory to complement an exception part of the inputted image, i.e., a part with concealment or noise, before recognizing the image. It is reported that a fine recognition result can be obtained with this technique even if there is concealment. However, it is necessary to execute calculation for recollecting the lost part of the data, which results in increasing the calculation amount. Further, it is necessary to prepare a large amount of data for training the self-associative memory so that it can recollect the lost part sufficiently.
Incidentally, the method as in Non-Patent Document 1, which divides the image into partial regions, recognizes those individually, and integrates each of the recognition results, changes its characteristics when the integration method is changed. As the methods with a low arithmetic operation amount for integrating a plurality of pattern recognition results, there are integration methods 1, 2, and 3 as follows.
Integration method 1: Employs majority rule on each of the partial recognition results.
Integration method 2: Finds total score based on the maximum value of each of partial recognition scores.
Integration method 3: Finds total score based on a product of occurrence probabilities of a recognition object category in each of the partial regions.
Integration method 4: Finds total score based on a product of occurrence probabilities of a partial recognition score.
It is widely known in the field of pattern recognition that the integration method 1 is an effective method. However, in a case of recognizing an object having partial concealment, this method is not capable of achieving recognition correctly if the number of regions with the concealment is more than a half of the number of all regions. In the meantime, the integration method 2 uses the maximum value of each partial recognition score, so that it is easier to make misrecognition when only one of the partial regions accidentally becomes likely to be the recognition object category. In the meantime, the integration method 3 can find the total score with a guarantee in terms of a probability theory, if each partial region is statistically independent. However, the integration method 3 is not designed by considering occurrence of partial concealment, so that the recognition score becomes deteriorated extremely only if there is concealment in a small number of areas. Note that one instance that utilizes the integration method 3 is depicted in an expression (1) of Non-Patent Document 3. In the meantime, the integration method 4 is a method which, when performing recognition based on Bayes' formula as shown in an expression (2) of Patent Document 1, calculates the total score based on the product of the occurrence probabilities of the partial recognition scores under a condition that it is the recognition object category as shown in an expression (3) of Patent Document 1. If a priori probability P (ki) in expression (2) of Patent Document 1 can be assumed to be the same for a recognition object category and a non-recognition object category, the integration method 4 becomes equivalent to the integration method 3.
“Category” is a term used in the field of pattern recognition, which indicates a classification of patterns. It may also be called a class. This is equivalent to a “kind” or a “group” in general terms. For example, when an image is classified depending on whether it is an automobile or not an automobile, “is an automobile” and “is not an automobile” are the two categories. Further, in a case with “is a child”, “is an adult”, “is an elderly person”, and “is not a human being”, there are four categories. As in those cases, the categories may be set in accordance with the content to be recognized. “Pattern” indicates various kinds of data such as images, sounds, and characters.    Patent Document 1: Japanese Unexamined Patent Publication 2001-283157    Non-Patent Document 1: Akira INOUE, “Face Recognition Using Local Area Matching”, Proceedings of 2003 IEICE General Conference, No. D-12-57, March 2003    Non-Patent Document 2: Takashi TAKAHASHI, Takio KURITA, Yukifumi IKEDA, “A Neural Network Classifier with Preprocessing to Correct Outliers in an Image” IEICE Transactions, Vol. J87-DII No. 5, pp. 1162-1169, 2004    Non-Patent Document 3: Akira MONDEN, Seiji YOSHIMOTO, “Similarity based on probability that two fingers have no correlation”, Technical Report of IEICE, Vol. 101 No. 525, pp. 53-58, December 2001